Delete triviaQARC.py

triviaQARC.py

@@ -1,109 +0,0 @@
-from collections import Counter
-import datasets
-
-class triviaQARC(datasets.Metric):
-    def _info(self):
-        return datasets.MetricInfo(
-            description= "Idk",
-            citation= "idk",
-            features=datasets.Features(
-                {
-                    "predictions": {"id": datasets.Value("string"), "prediction_text": datasets.Value("string")},
-                    "references": {
-                        "id": datasets.Value("string"),
-                        "answers": datasets.features.Sequence(
-                            {
-                                "text": datasets.Value("string"),
-                                "answer_start": datasets.Value("int32"),
-                            }
-                        ),
-                    },
-                }
-            ),
-        )
-
-    def _compute(self, predictions, references):
-        pred_dict = {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
-        dataset = [
-            {
-                "paragraphs": [
-                    {
-                        "qas": [
-                            {
-                                "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
-                                "id": ref["id"],
-                            }
-                            for ref in references
-                        ]
-                    }
-                ]
-            }
-        ]
-        score = evaluate(dataset=dataset, predictions=pred_dict)
-        return score
-
-def evaluate(dataset, predictions):
-    f1 = exact_match = total = recall = precision= 0
-    for article in dataset:
-        for paragraph in article["paragraphs"]:
-            for qa in paragraph["qas"]:
-                total += 1
-                if qa["id"] not in predictions:
-                    message = "Unanswered question " + qa["id"] + " will receive score 0."
-                    print(message, file=sys.stderr)
-                    continue
-                ground_truths = list(map(lambda x: x["text"], qa["answers"]))
-                prediction = predictions[qa["id"]]
-                exact_match += metric_max_over_ground_truths(exact_match_score, prediction, ground_truths)
-                temp_f1, temp_precision, temp_recall = metric_max_over_ground_truths(f1_score, prediction, ground_truths)
-                f1 += temp_f1
-                precision += temp_precision
-                recall += temp_recall
-
-    exact_match = 100.0 * exact_match / total
-    f1 = 100.0 * f1 / total
-
-    return {"exact_match": exact_match, "f1": f1, "recall": recall, "precision": precision}
-
-def normalize_answer(s):
-    """Lower text and remove punctuation, articles and extra whitespace."""
-
-    def remove_articles(text):
-        return re.sub(r"\b(a|an|the)\b", " ", text)
-
-    def white_space_fix(text):
-        return " ".join(text.split())
-
-    def remove_punc(text):
-        exclude = set(string.punctuation)
-        return "".join(ch for ch in text if ch not in exclude)
-
-    def lower(text):
-        return text.lower()
-
-    return white_space_fix(remove_articles(remove_punc(lower(s))))
-
-
-def f1_score(prediction, ground_truth):
-    prediction_tokens = normalize_answer(prediction).split()
-    ground_truth_tokens = normalize_answer(ground_truth).split()
-    common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
-    num_same = sum(common.values())
-    if num_same == 0:
-        return 0
-    precision = 1.0 * num_same / len(prediction_tokens)
-    recall = 1.0 * num_same / len(ground_truth_tokens)
-    f1 = (2 * precision * recall) / (precision + recall)
-    return f1, precision, recall
-
-
-def exact_match_score(prediction, ground_truth):
-    return normalize_answer(prediction) == normalize_answer(ground_truth)
-
-
-def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
-    scores_for_ground_truths = []
-    for ground_truth in ground_truths:
-        score = metric_fn(prediction, ground_truth)
-        scores_for_ground_truths.append(score)
-    return max(scores_for_ground_truths)